import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head(100)
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 95 | 2019-10-28 | 1.155603 | 1.461829 | 1.457474 | 1.036232 | 1.365827 | 1.629663 |
| 96 | 2019-11-04 | 1.189743 | 1.486514 | 1.452951 | 1.021354 | 1.388495 | 1.655063 |
| 97 | 2019-11-11 | 1.211063 | 1.518629 | 1.415209 | 1.044153 | 1.404972 | 1.700533 |
| 98 | 2019-11-18 | 1.175199 | 1.495886 | 1.420278 | 1.064062 | 1.478547 | 1.696224 |
| 99 | 2019-11-25 | 1.183927 | 1.527143 | 1.465089 | 1.079154 | 1.498452 | 1.716521 |
100 rows × 7 columns
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
plot = stocks.plot(x='date', y='GOOG')
plot.set_title('Google Stock')
plot.set_ylabel('Stock Value')
plt.rcParams["figure.figsize"] = (15,30)
plt.show()
# Towards point 0-98
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
plot = stocks.plot(x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'])
plot.set_title('Google Stock')
plot.set_ylabel('Stock Value')
plt.rcParams["figure.figsize"] = (15,10)
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
# YOUR CODE HERE
print("Question: What are the correlations between total bill and tips, sorted by day and gender?")
splot = sns.FacetGrid(tips, col='day', hue='sex')
splot.map(sns.scatterplot, 'total_bill', 'tip')
splot.add_legend()
plt.show()
Question: What are the correlations between total bill and tips, sorted by day and gender?
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
df = px.data.stocks()
fig = px.line(df, x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'])
fig.show()
# YOUR CODE HERE
df = px.data.tips()
fig = px.scatter(df, x='total_bill', y='tip', color='sex', facet_col='day')
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# year_2007 = df['year'] == 2007
# df[year_2007]
year_2007_bar = px.data.gapminder().query('year == 2007')
fig = px.bar(year_2007_bar, x='continent', y='pop', color='continent', text = 'country')
fig.update_xaxes(categoryorder = "total descending")
fig.show()